Triple
T29865777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Singani |
E758451
|
entity |
| Predicate | temperatureOfService |
P66977
|
FINISHED |
| Object | served chilled |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: served chilled | Statement: [Singani, temperatureOfService, served chilled]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureOfService Context triple: [Singani, temperatureOfService, served chilled]
-
A.
recommendedServingTemperature
chosen
Indicates the temperature at which something (typically food or drink) is advised to be served for optimal use or enjoyment.
-
B.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
C.
servedHot
Indicates that something is provided or presented in a heated or warm state, suitable for immediate consumption.
-
D.
temperatureConditions
Indicates the specific thermal or weather-related temperature state or range affecting an entity or situation.
-
E.
hasTemperatureCategory
Indicates that an entity is associated with a specific qualitative temperature classification (e.g., hot, cold, warm).
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f2245b4dec8190b85f664d918a00a5 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67689a3908190bdef1a1108f42f87 |
completed | May 2, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69f66ac32b60819092290b2de35988d3 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:51 p.m.